Then, a Continuous Skipgram embedding model [1]} was trained on this corpus, using a vector size of 300, 10 negative samples, a symmetric window of 2 words, no down-sampling, and 5 iterations over the training data. Words with corpus frequency less than 5 were ignored. This model represents the semantics of the words it contains. But at the same time, for each word, a PoS tag is known (from the BNC annotation). It means that is is possible to test how good the word embeddings are in grouping words according to their parts of speech.
